Neural Network Modeling for an Intelligent Recommendation System
Supporting SRM for Universities in Thailand
KANOKWAN KONGSAKUN*
1. School of Information Technology,
Murdoch University, South Street, Murdoch, WA, AUSTRALIA 6150
[email protected] http://www.murdoch.edu.au
2. Department of Business Computer, Hatyai University
Hatyai, Songkla, THAILAND 90110
Tel: (61)4 0132 6356
[email protected] http://www.hu.ac.th
CHUN CHE FUNG
School of Information Technology, Murdoch University
South Street, Murdoch, WA, AUSTRALIA 6150
[email protected] http://www.murdoch.edu.au
Abstract: - In order to support the academic management processes, many universities in Thailand have
developed innovative information systems and services with an aim to enhance efficiency and student
relationship. Some of these initiatives are in the form of a Student Recommendation System (SRM).
However, the success or appropriateness of such system depends on the expertise and knowledge of the
counselor. This paper describes the development of a proposed Intelligent Recommendation System
(IRS) framework and experimental results. The proposed system is based on an investigation of the
possible correlations between the students’ historic records and final results. Neural Network techniques
have been used with an aim to find the structures and relationships within the data, and the final Grade
Point Averages of freshmen in a number of courses are the subjects of interest. This information will
help the counselors in recommending the appropriate courses for students thereby increasing their
chances of success.
Key-Words: - Data Mining, Neural Network, Student Relationship Management, Intelligent
Recommendation System
1 Introduction The growing complexity of technology in
educational institutions creates opportunities for
substantial improvements for management and
information systems. Many designs and techniques
have allowed for better results in analysis and
predictions. With this in mind, universities in
Thailand are working hard to improve the quality of
education and many institutes are focusing on how
to increase the student retention rates and the
number of completions. In addition, a university’s
performance is also increasingly being used to
measure its ranking and reputation [1]. One form of
service which is normally provided by all
universities is Student Counseling. Archer and
Cooper [2] stated that the provision of counseling
services is an important factor contributing to
students’ academic success. In addition, Urata and
Takano [3] stated that the essence of student
counseling should include advices on career
guidance, identification of learning strategies,
handling of inter-personal relation, along with self-
understanding of the mind and body. It can be said
that a key aspect of student services is to provide
course guidance as this will assist the students in
their course selection and future university
experience.
On the other hand, many students have chosen
particular courses of study just because of perceived
job opportunities, peer pressure and parental advice.
Issues may arise if a student is not interested in the
course, or if the course or career is not suitably
matched with the student’s capability[4]. In
Thailand’s tertiary education sector, teaching staff
may have insufficient time to counsel the students
due to high workload and there are inadequate tools
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to support them. Hence, it is desirable that some
forms of intelligent recommendation tools could be
developed to assist staff and students in the
enrolment process. This forms the motivation of this
research.
One of the initiatives designed to help students
and staff is the Student Recommendation System
(SRM). Such system could be used to provide
course advice and counseling for freshmen in order
to achieve a better match between the student’s
ability and success in course completion. In the case
of Thai universities, this service is normally
provided by counselors or advisors who have many
years of experience within the organisation.
However, with increasing number of students and
expanded number of choices, the workload on the
advisors is becoming too much to handle. It
becomes apparent that some forms of intelligent
system will be useful in assisting the advisors.
In this paper, a proposed intelligent
recommendation system is reported. This paper is
structured as follows. Section 2 describes literature
reviews of Student Relationship Management
(SRM) in universities and issues faced by Thai
university students. Section 3 describes Neural
Network techniques which are used in the reported
Intelligent Recommendation System, and Section 4
focuses on the proposed framework, which presents
the main idea and the research methodology.
Section 5 describes the experiments and the results.
This paper then concludes with discussions on the
work to be undertaken and future development.
2 Literature Review
2.1 Student Relationship Management in
Universities According to literature, the problem of low student
retention in higher education could be attributed to
low student satisfaction, student transfers and drop-
outs [5]. This issue leads to a reduction in the
number of enrolments and revenue, and increasing
cost of replacement. On the other hand, it was found
that the quality and convenience of support services
are other factors that influence students to change
educational institutes [6]. Consequently, the concept
of SRM has been implemented in various
universities so as to assist the improvement of the
quality of learning processes and student activities.
Definitions of SRM have been adopted from the
established practices of Customer Relationship
Management (CRM) which focuses on customers
and are aimed to establish effective competition and
new strategies in order to improve the performance
of a firm [7]. In the case of SRM, the context is
within the education sector. Although there have
been many research focused on CRM, few research
studies have concentrated on SRM. In addition, the
technological supports are inadequate to sustain
SRM in universities. For instance, a SRM system’s
architecture has been proposed so as to support the
SRM concepts and techniques that assist the
university’s Business Intelligent System [8]. This
project provided a tool to aid the tertiary students in
their decision-making process. The SRM strategy
also provided the institution with SRM practices,
including the planned activities to be developed for
the students, as well other relevant participants.
However, the study verified that the technological
support to the SRM concepts and practices were
insufficient at the time of writing [8].
In the context of educational institutes, the
students may be considered having a role as
“customers”, and the objective of Student
Relationship Management is to increase their
satisfaction and loyalty for the benefits of the
institute. SRM may be defined under a similar view
as CRM and aims at developing and maintaining a
close relationship between the institute and the
students by supporting the management processes
and monitoring the students’ academic activities
and behaviors. Piedade and Santos (2008)
explained that SRM involves the identification of
performance indicators and behavioral patterns that
characterize the students and the different situations
under which the students are supervised. In
addition, the concept of SRM is “understood as a
process based on the student acquired knowledge,
whose main purpose is to keep a close and effective
students institution relationship through the closely
monitoring of their academic activities along their
academic path” [9]. Hence, it can be said that SRM
can be utilised as an important means to support
and enhance a student’s satisfaction. Since
understanding the needs of the students is essential
for their satisfaction, it is necessary to prepare
strategies in both teaching and related services to
support Student Relationship Management. This
paper therefore proposes an innovative information
system to assist students in universities in order to
support the SRM concept.
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2.2 Issues Faced By Thai University
Students Prior studies have studied issues faced by Thai
students during their time in the universities. For
example, Sarawut [10] studied the causes of
dropouts and program incompletion among
undergraduate students from the Faculty of
Engineering, King Mongkut’s University of
Technology North Bangkok, that the general reason
for underachievement is a teaching and learning
issue. Furthermore, the study shows that three
unaccomplished group have different reasons. The
main reason of the first group is a student’s attitude
towards the field of study. This group has
perception that their field of study is too hard. The
main reasons of the second and third group are
related to teaching and learning. Hence, this
indicates the need to match the course requirements
and academic capabilities of the students.
Another study at Dhurakij Pundit University,
Thailand looked at the relationship between learning
behaviour and low academic achievement (below
2.0 GPA) of the first year students in the regular
four-year undergraduate degree programs. The
results indicated that students who had low
academic achievement had a moderate score in
every aspect of learning behaviour. On average, the
students scored highest in class attendance, followed
by the attempt to spend more time on study after
obtaining low examination grades. Some of the
problems and difficulties that mostly affected
students’ low academic achievement were the
students’ lack of understanding of the subject and
lack of motivation and enthusiasm to learn [11].
Moreover, some other studies had focused on
issues relating to students’ backgrounds prior to
their enrolment, which may have effects on the
progress of the students’ studies. For example, a
research group from the Department of
Education[12], Thailand studied the backgrounds of
289,007 Grade 12 students which may have affected
their academic achievements. The study showed that
the factors which could have effects on the
academic achievement of the students may be
attributed to personal information such as gender
and interests, parental factors such as their jobs and
qualifications, and information on the schools such
as their sizes, types and ranking.
Therefore, in the recruitment and enrolment of
students in higher education, it is necessary to meet
the student’s needs and to match their capability
with the course of their choice. The students’
backgrounds may also have a part to play in the
matching process. Understanding the student’s
needs will implicitly enhance the student’s learning
experience and increase their chances of success,
and thereby reduce the wastage of resources due to
dropouts, and change of programs. These factors are
therefore taken into consideration in the proposed
recommendation system in this study.
3 Neural Network Based Intelligent
Recommendation System to Support
SRM In term of education systems, Ackerman and
Schibrowsky [13] have applied the concept of
business relationships and proposed the business
relationship marketing framework. The framework
provided a different view on retention strategies and
an economic justification on the need for
implementing retention programs. The prominent
result is the improvement of graduation rates by
65% by simply retaining one additional student out
of every ten. The researcher added that this
framework is appropriate both on the issues of
places on quality of services. Although some
problems could not be solved directly, it is
recognized that Information and Communication
Technologies (ICT) can be used and contributes
towards maintaining a stronger relationship with
students in the educational systems [8].
In this study, a new intelligent Recommendation
System is proposed to support universities students
in Thailand. This System is a hybrid system which
is based by Neural Network and Data Mining
techniques; however, this paper only focuses on the
aspect of Neural Network (NN) techniques.
With respect to the Neural Network algorithm
that was used in this study, the back propagation
learning algorithm (BP) was used to perform the
supervised learning process [14]. The
implementation of back propagation learning
updates the network weights and biases in the
direction in which the system performance increases
most rapidly. An iteration of this algorithm can be
written as
Bm+1=Bm - ∞mAm (1)
Where Bm is a vector of the current weights and
biases, Am is the current gradient, and ∞m is the
learning rate. This study used a feed-forward
network architecture and the Mean Square Error
(MSE) to define the accuracy of the models.
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4 The Proposed Framework Several solutions have been proposed to support
SRM in the universities; however, not many systems
in Thailand have focused on recommendation
systems using historic records from graduated
students. A recommendation system could apply
statistical, artificial intelligence and data mining
techniques by making appropriate recommendation
for the students. Figure 1 illustrates the proposed
recommendation system architecture. This proposal
aims to analyse student background such as the high
school where the student studied previously, school
results and student performance in terms of GPA’s
from the university’s database. The result can then
be used to match the profiles of the new students. In
this way, the recommendation system is designed to
provide suggestions on the most appropriate courses
and subjects for the students, based on historical
records from the university’s database.
Fig.1 Proposed Hybrid Recommendation System Framework to Support Student
Relationship Management (SRM)
4.1 Data-Preprocessing Initially, data on the student records are collected
from the university enterprise database. The data is
then re-formatted in the stage of data transformation
in order to prepare for processing by subsequent
algorithms. In the data cleaning process, the
parameters used in the data analysis are identified
and the missing data are either eliminated or filled
with null values [15]. Preparation of analytical
variables is done in the data transformation step or
being completed in a separate process. Integrity of
the data is checked by validating the data against the
Student Historic data
…
Sub-model 1:
Computer
Business
Sub-model 2:
Information
Technology
Sub-model 3:
Accounting
Sub-model 14:
Communication Art
3. Intelligent Prediction Model
4. Online
Intelligent Recommendation
System
1.Course Prediction for freshmen
Overall
GPA
Prediction
Course
Ranking
Prediction
2.GPA Prediction for students
GPA prediction for year 1
GPA prediction for year 2
GPA prediction for year 3
GPA prediction for year 4
1. Data Pre-processing
Data
Transformation
Data
Cleaning
Neural
Network
2. Data Analysis
Association
Rules
Decision
Tree
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legitimate range of values and data types. Finally,
the data is separated randomly into training,
validation and testing data for processing by the
Neural Network.
4.2 Data Analysis In Fig. 1, the Association rules, Decision Tree and
Neural Network are used to analyse the input data;
however, this paper focuses on Neural Network
which uses the backpropagation algorithm to
classify the data and to establish the approximate
function. The backpropagation algorithm is a
multilayer network with nonlinear differentiable
transfer function and it uses log-sigmoid as the
transfer function, logsig. In the training process, the
backpropagation training functions in the feed-
forward networks is used to predict the output based
on the input data. The appropriate data series to be
trained by Neural Network are significant.
4.3 Intelligent Prediction Model The Integrated Prediction Model is composed of
two parts: Course Prediction for freshmen, and GPA
Prediction for students (years 1 to 4) respectively.
Part 1 focuses on the course prediction for
freshmen and it is composed of two sections, which
are the Course Ranking Prediction, and the Overall
GPA prediction respectively. In Section 1, the
Course Ranking Prediction is analysed by
Association Rules and Decision Trees. The output
of the prediction is an indication of the ranking of
the appropriate courses. In the section 2 (Overall
GPA Prediction) is analysed by the Neural Network
technique. The output of this prediction is in terms
of an expected overall GPA. The results of both
parts can be used as suggestions to the freshmen
during the enrolment process. Some example results
from Section 2 are shown in this paper, and the
input data of these 2 sections in the model are
shown in Table 1.
Another part of the framework focuses on GPA
prediction for students in each year. They are the
GPA Prediction for students from year 1, year 2,
year 3 and year 4 respectively. After the students
selected the course to study and completed the
enrolment process, the GPA prediction for year 1
results can be used to monitor the performance of
this group of students. The input data of this
process is the same as the one shown in Table 1,
with the addition of the GPA scores from the
previous yea. These are used as the extended
features in the input to the neural network model.
The result of the prediction is the GPA score of the
year. In the same way, the system may be used to
perform a GPA prediction for Year 2 based on
results from the first year. The inputs for this
module are shown in Table 1. Similar approach can
be adopted for the prediction of Year 3 and 4
results. Some example results this part are shown in
this paper.
To address the issue of imbalanced number of
students in each course, the prediction model shown
in Fig. 1 can be duplicated for different departments.
The models’ computation is entirely data-driven and
not based on subjective opinion, hence, the
prediction models are unbiased and they will be
used as an integral part of an online intelligent
recommendation system.
4.4 Online-Intelligent Recommendation
System It is planned that the new intelligent
Recommendation Models will form an integral part
of an online system for a private university in
Thailand. The developed system will be evaluated
by the university management and feedback from
experienced counselors will be sought. The
proposed system will be available for use by new
students who will access the online-application in
their course selection during the enrolment process.
As for the prediction of the Year 2 and subsequent
years’ results, this could be used by the counselors,
staff and university management to provide supports
for students who are likely to need help with their
studies. This information will enable the university
to better focus on the utilisation of their resources.
In particular, this could be used to improve the
retention rate by providing additional supports to the
group of students who may be at risk.
5 Experiment Design The data preparation and selection process involves
a dataset of 3,550 student records from five
academic years. All the student data have included
records from the first year to graduation. Due to
privacy issue, the data in this study do not indicate
any personal information, and no student is
identified in the research. The university has
randomised the data, and all private information has
been removed. Example data from the dataset is
shown below.
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Table 1 Examples of training sample dataset
Uni
ID
Input data: previous school data target
GPA
Type
of
school
Number
of
Awards
Talent
&
Interest
Channels
Admission
Round
Guardian
Occupation
Gender
Uni
GPA
4800 2.35 C 0.2 1 Poster 1 Police F 3.75
4801 3.55 B 0.3 4 Brochure 2 Governor M 3.05
5001 2.55 A 0.9 3 Friend 5 Teacher F 2.09
5002 2.75 G 0.4 5 Family 4 Nurse F 2.58
5003 3.00 F 0.2 7 Newspaper 3 Teacher M 2.77
5101 2.00 E 0.1 2 others 1 Farmer F 2.11
Table 1 shows the randomized student ID, GPA
from previous study, the type of school, awards
received, talent and interest, channels to know the
university, admission round, Guardian Occupation,
Gender and Overall GPA from university. Table 2
provides the definitions for the variables used in the
above table.
Table 2 Definitions of Variables
No. Variables Definition
1. Uni ID Randomized Student ID which is not included in the clustering
process. They are only used as an identification of different students
2. GPA Overall GPA results from previous study prior to admission to
university
3. Type of school The school types are separated as follows
A: High School
B:Technical College
C: Commercial College
D: Open School
E: Sports, Thai Dancing, Religion or Handcraft Training Schools
F: Other Universities
(change universities or courses)
G: Vocation Training Schools
4. Number of Awards Awards that students have received from previous study
(normalized between 0.0 to 4.0, 0.0 – received no award, 4.0 –
received max no. of awards in the dataset)
5. Talent and Interest (in
Group number)
Talent and the interest(1= sports, 2=music and entertainment, 3=
presentation, 4=academic, 5=others, 6= involved with 2 to 3 items
of talents and interests, 7= involved with more than 3 talents and
interests)
6 Channels The channels to know the university such as television, family
7 Admission Round Admission round of each university which can be round 1 to 5
8 Guardian Occupation The occupation of Guardian such as teacher, governor
9. Gender Gender: Female or Male
10. Uni GPA Overall GPA in university which the range is from 0 to 4
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Fig.2 Number of samples in each department
The student records have been divided into 60% of
training data, 20% of testing datasets and 20% of
data validation randomly. The dataset includes both
qualitative and quantitative information in Table 1
and 2. In terms of training, a maximum of 50,000
epochs is selected or until the network sum square
error (SSE) falls beneath 0.1. The learning rate of
0.01 is set. This is to ensure that input vectors are
being classified correctly. This study used a two
layer feed forward network architecture with
between a maximum of 20 neurons in the hidden
layer (A half of input and output). Moreover, this
study used the Mean Square Error (MSE) to define
the accuracy of the models.
6 Results
6.1 Results of Course Prediction for
freshmen: Overall GPA Prediction Based on Mean System Error (MSE), the
experimental results have shown that the Neural
Network based models can be utilised to predict the
GPA results of students with a good degree of
accuracy.
0
200
400
600
800
1000
1200
Number of samples
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Fig.3 Comparison of Mean Square Error of each Department
In Fig. 3, it is shown that the highest value of MSE
is 0.68 based on data from the Department of
Teaching Profession. On the otherhand, the lowest
value is 0.2 which was found in many schools such
as the Department of Management. The average of
Mean Square Error (MSE) of all models is 0.028.
The overall results obtained indicated reasonable
prediction results.
The following figure is an example of the results
obtained from the model for the Business Computer
course.
Fig.4 Example result from the model for the Business Computer Department
The chart shows that Mean Squared Error
(MSE) goes down slightly from 0.1 to 0.001. The
MSE starts at a large value and decreases to a small
value. The training stopped when the validation
error decreased after 738 Epochs. The plot shows
two lines, one for the training and the other for
the validation. However, testing was carried out in
the final step of the experiment in each model,
which used 20% of data. From the experiment, it
was observed that no significant over-fitting
occurred by iteration 738, where the best validation
performance occurred.
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
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Fig.5 Example result of training state business computer department
Fig.6 An illustration of Regressions between training, validation and testing data for the Business
Computer Department
The chart shows the regression of training and
validation results based on the test data. The R-
value of training state and validation are 0.921 and
0.881 respectively. Finally, all R-values are over
0.90 and the outputs seem to track the targets
reasonably well.
6.2 Results of GPA Prediction for students The experimental results have shown that the Neural
Network based models can also be utilised to predict
the GPA results of students each year. The bars as
shown in fig 4
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Fig.7 The Mean Square Error of the model of GPA Prediction for students
Fig 7 shows a comparison of the Mean Square Error
(MSE) from each department in four years. The
vertical columns are the Mean Square Errors of each
department, and each bar shows MSE of year 1 to
year 4 respectively. Comparatively, the highest
value of MSE is 0.75 from the Department of
Communication Arts in year 1, while the lowest
value is 0.2 which was found in many departments.
The lowest in this case is Year 1 result from the
Department of Management and Accounting.
Moreover, MSE of the models of Management,
Accounting and Business computer department are
smaller. It may be due the larger number of the
samples. Overall, the Neural Network Models have
indicated that they possess the capability to present
reasonable prediction results.
7 Conclusions This article describes a proposal on a
recommendation system in support of SRM and to
address issues related to the problem of course
advice or counseling for university students in
Thailand. The recent work is focusing on the
development and implementation of each process in
the framework. The experiments have been based on
Neural Network models and the accuracy of the
prediction model is improved. It is expected that the
recommendation system will provide a useful
service for the university management, course
counselors, academic staff and students. The
proposed system will also support Student
Relationship Management strategies among the Thai
private universities.
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